options(future.globals.maxSize = 74 * 1024^3) # 55 GB
getOption("future.globals.maxSize") #59055800320
## [1] 79456894976

1 Load Raw Macula Densa .rds file

SO <- readRDS(here("outputs", "so_merge_raw.rds"))

head(SO@meta.data)
DimPlot(SO)

1.1 nFeatures, nCounts, and percent.mt

VlnPlot(SO, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), group.by = "orig.ident")

VlnPlot(SO, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), group.by = "sample")

FeaturePlot(SO, "nCount_RNA")

FeaturePlot(SO, "nFeature_RNA")

FeaturePlot(SO, "percent.mt")

1.2 Identify clusters

1.2.1 Cluster 0: MD1

1.2.2 Cluster 1: MD2

1.2.3 Cluster 2: MD: Activated (cFos)

1.2.4 Cluster 3: EC (Pecam1)

1.2.5 Cluster 4: Stroma (Vim)

1.2.6 Cluster 5: CD (Aqp2)

1.2.7 Cluster 6: IC (Kit)

1.2.8 Cluster 7: PT (Slc34a1)

SO.markers <- FindAllMarkers(SO, only.pos = TRUE)
SO.markers %>%
    group_by(cluster) %>%
    dplyr::filter(avg_log2FC > 1)
SO.markers %>%
    group_by(cluster) %>%
    dplyr::filter(avg_log2FC > 1) %>%
    slice_head(n = 5) %>%
    ungroup() -> top10
DoHeatmap(SO, features = top10$gene) + NoLegend()

markers.to.plot1 <- c("Lrp2",         # PT
                      "Slc5a12",      # PT-S1
                      "Slc13a3",      # PT-S2
                      "Slc16a9",      # PT-S3
                      "Havcr1",       # Injured PT
                      "Epha7",        # dTL
                      "Cryab",        # dTL
                      "Cdh13",        # dTL1
                      "Slc14a2",      # dTL2
                      "Slc12a1",      # TAL
                      "Umod",         # TAL, DCT1
                      "Egf",          # TAL, DCT1,
                      "Cldn10",       # TAL
                      "Cldn16",       # TAL
                      "Nos1",         # MD
                      "Slc12a3",      # DCT
                      "Pvalb",        # DCT1
                      "Slc8a1",       # DCT2, CNT
                      "Aqp2",         # PC
                      "Slc4a1",       # IC-A
                      "Slc26a4",      # IC-B
                      "Nphs1",        # Podo
                      "Ncam1",        # PEC
                      "Flt1",         # Endo
                      "Emcn",         # Glom Endo
                      "Kdr",          # Capillary Endo
                      "Pdgfrb",       # Perivascular
                      "Pdgfra",       # Fib
                      "Piezo2",       # Mesangial
                      "Acta2",        # Mural
                      "Ptprc",        # Immune
                      "Cd74",         # Macrophage
                      "Skap1",        # B/T Cells 
                      "Upk1b",        # Uro
                      "Top2a"         # Proliferation
)
                      
DotPlot(SO,
features = markers.to.plot1,
dot.scale = 8,
dot.min = 0,
scale.max = 100,
scale.min = 0,
col.min = -2.5,
col.max = 2.5)+
coord_flip()

1.3 Check FeaturePlot/VlnPlot

### Cluster 2: MD: Activated (cFos)
### Cluster 3: 
### Cluster 4: EC (Pecam1)
### Cluster 5: CD (Aqp2)
### Cluster 6: IC (Kit)
### Cluster 7: PT (Slc34a1)

FeaturePlot(SO, "Nos1")

VlnPlot(SO, features = "Nos1")

FeaturePlot(SO, "Pappa2")

VlnPlot(SO, features = "Pappa2")

FeaturePlot(SO, "Slc12a1")

VlnPlot(SO, features = "Slc12a1")

FeaturePlot(SO, "Fos")

VlnPlot(SO, features = "Fos")

FeaturePlot(SO, "Pecam1")

VlnPlot(SO, features = "Pecam1")

FeaturePlot(SO, "Aqp2")

VlnPlot(SO, features = "Aqp2")

FeaturePlot(SO, "Kit")

VlnPlot(SO, features = "Kit")

FeaturePlot(SO, "Slc34a1")

VlnPlot(SO, features = "Slc34a1")

2 Filter non-MD Cells and re-cluster

SO2 <- subset(SO, idents = c("4", "5", "6", "7"), invert = TRUE)

DimPlot(SO2)

SO2 <- SCTransform(SO2) %>%
    RunPCA() %>%
    FindNeighbors(dims = 1:30) %>%
    FindClusters() %>%
    RunUMAP(dims = 1:30)
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## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
## 
## Number of nodes: 11529
## Number of edges: 391481
## 
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.7904
## Number of communities: 15
## Elapsed time: 1 seconds
DimPlot(SO2)

markers.to.plot2 <- c("Lrp2",         # PT
                      "Slc5a12",      # PT-S1
                      "Slc13a3",      # PT-S2
                      "Slc16a9",      # PT-S3
                      "Havcr1",       # Injured PT
                      "Epha7",        # dTL
                      "Cryab",        # dTL
                      "Cdh13",        # dTL1
                      "Slc14a2",      # dTL2
                      "Slc12a1",      # TAL
                      "Umod",         # TAL, DCT1
                      "Egf",          # TAL, DCT1,
                      "Cldn10",       # TAL
                      "Cldn16",       # TAL
                      "Nos1",         # MD
                      "Slc12a3",      # DCT
                      "Pvalb",        # DCT1
                      "Slc8a1",       # DCT2, CNT
                      "Aqp2",         # PC
                      "Slc4a1",       # IC-A
                      "Slc26a4",      # IC-B
                      "Nphs1",        # Podo
                      "Ncam1",        # PEC
                      "Flt1",         # Endo
                      "Emcn",         # Glom Endo
                      "Kdr",          # Capillary Endo
                      "Pdgfrb",       # Perivascular
                      "Pdgfra",       # Fib
                      "Piezo2",       # Mesangial
                      "Acta2",        # Mural
                      "Ptprc",        # Immune
                      "Cd74",         # Macrophage
                      "Skap1",        # B/T Cells 
                      "Upk1b",        # Uro
                      "Top2a",         # Proliferation
                      "Cldn5",
                      "Jun",
                      "Fosb"
)
                      
DotPlot(SO2,
features = markers.to.plot2,
dot.scale = 8,
dot.min = 0,
scale.max = 100,
scale.min = 0,
col.min = -2.5,
col.max = 2.5)+
coord_flip()

FeaturePlot(SO2, "Slc12a3")

FeaturePlot(SO2, "Fosb")

FeaturePlot(SO2, "Cldn5")

DimPlot(SO2, group.by = "treatment")

3 Subset and recluster

SO3 <- subset(SO2, idents = c("13"), invert = TRUE)

DimPlot(SO3)

SO3 <- SCTransform(SO3) %>%
    RunPCA() %>%
    FindNeighbors(dims = 1:10) %>%
    FindClusters(resolution = .3) %>%
    RunUMAP(dims = 1:10)
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## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
## 
## Number of nodes: 11471
## Number of edges: 355404
## 
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.8631
## Number of communities: 6
## Elapsed time: 1 seconds
DimPlot(SO3)

SO.markers <- FindAllMarkers(SO3, only.pos = TRUE)

SO.markers %>%
    group_by(cluster) %>%
    dplyr::filter(avg_log2FC > .5) %>%
    slice_head(n = 10) %>%
    ungroup() -> top10

DoHeatmap(SO3, features = top10$gene) + NoLegend()

FeaturePlot(SO3, "Nos1")

VlnPlot(SO3, "Nos1", split.by = "treatment")

VlnPlot(SO3, "Nos1", split.by = "sample")

FeaturePlot(SO3, "Pappa2")

VlnPlot(SO3, "Pappa2", split.by = "treatment")

VlnPlot(SO3, "Pappa2", split.by = "sample")

FeaturePlot(SO3, "Atp1a1")

VlnPlot(SO3, "Atp1a1", split.by = "treatment")

VlnPlot(SO3, "Atp1a1", split.by = "sample")

FeaturePlot(SO3, "Cxcl10")

VlnPlot(SO3, "Cxcl10", split.by = "treatment")

VlnPlot(SO3, "Cxcl10", split.by = "sample")

FeaturePlot(SO3, "Cox5b")

VlnPlot(SO3, "Cox5b", split.by = "treatment")

VlnPlot(SO3, "Cox5b", split.by = "sample")

VlnPlot(SO3, "percent.mt", split.by = "treatment")

# Session Info

sessionInfo()
## R version 4.3.1 (2023-06-16 ucrt)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 11 x64 (build 22631)
## 
## Matrix products: default
## 
## 
## locale:
## [1] LC_COLLATE=English_United States.utf8 
## [2] LC_CTYPE=English_United States.utf8   
## [3] LC_MONETARY=English_United States.utf8
## [4] LC_NUMERIC=C                          
## [5] LC_TIME=English_United States.utf8    
## 
## time zone: America/Los_Angeles
## tzcode source: internal
## 
## attached base packages:
## [1] grid      stats4    stats     graphics  grDevices utils     datasets 
## [8] methods   base     
## 
## other attached packages:
##  [1] CellChat_2.1.2              igraph_2.1.4               
##  [3] ggtext_0.1.2                ComplexHeatmap_2.16.0      
##  [5] UpSetR_1.4.0                EnhancedVolcano_1.18.0     
##  [7] lubridate_1.9.4             forcats_1.0.0              
##  [9] purrr_1.0.4                 readr_2.1.5                
## [11] tidyr_1.3.1                 tidyverse_2.0.0            
## [13] enrichplot_1.20.3           clusterProfiler_4.8.3      
## [15] gplots_3.2.0                kableExtra_1.4.0           
## [17] ggvenn_0.1.10               data.table_1.17.0          
## [19] readxl_1.4.3                openxlsx_4.2.8             
## [21] car_3.1-3                   carData_3.0-5              
## [23] stringr_1.5.1               here_1.0.1                 
## [25] DESeq2_1.40.2               SummarizedExperiment_1.30.2
## [27] Biobase_2.60.0              MatrixGenerics_1.12.3      
## [29] matrixStats_1.5.0           GenomicRanges_1.52.1       
## [31] GenomeInfoDb_1.36.4         IRanges_2.34.1             
## [33] S4Vectors_0.38.2            BiocGenerics_0.46.0        
## [35] ggrepel_0.9.6               RColorBrewer_1.1-3         
## [37] ggpmisc_0.6.1               ggpp_0.5.8-1               
## [39] tibble_3.2.1                BiocManager_1.30.25        
## [41] ggplot2_3.5.1               knitr_1.49                 
## [43] patchwork_1.3.0             SeuratObject_5.0.2         
## [45] Seurat_4.4.0                dplyr_1.1.4                
## 
## loaded via a namespace (and not attached):
##   [1] goftest_1.2-3           Biostrings_2.68.1       vctrs_0.6.5            
##   [4] spatstat.random_3.3-2   digest_0.6.37           png_0.1-8              
##   [7] shape_1.4.6.1           registry_0.5-1          deldir_2.0-4           
##  [10] parallelly_1.42.0       MASS_7.3-60             reshape2_1.4.4         
##  [13] httpuv_1.6.15           foreach_1.5.2           qvalue_2.32.0          
##  [16] withr_3.0.2             ggrastr_1.0.2           xfun_0.51              
##  [19] ggfun_0.1.8             ggpubr_0.6.0            survival_3.5-5         
##  [22] memoise_2.0.1           ggbeeswarm_0.7.2        MatrixModels_0.5-3     
##  [25] gson_0.1.0              systemfonts_1.2.1       tidytree_0.4.6         
##  [28] zoo_1.8-13              GlobalOptions_0.1.2     gtools_3.9.5           
##  [31] pbapply_1.7-2           Formula_1.2-5           KEGGREST_1.40.1        
##  [34] promises_1.3.2          httr_1.4.7              downloader_0.4         
##  [37] rstatix_0.7.2           globals_0.16.3          fitdistrplus_1.2-2     
##  [40] rstudioapi_0.17.1       miniUI_0.1.1.1          generics_0.1.3         
##  [43] DOSE_3.26.2             ggalluvial_0.12.5       zlibbioc_1.46.0        
##  [46] ggraph_2.2.1            polyclip_1.10-7         GenomeInfoDbData_1.2.10
##  [49] xtable_1.8-4            doParallel_1.0.17       evaluate_1.0.3         
##  [52] S4Arrays_1.2.0          hms_1.1.3               irlba_2.3.5.1          
##  [55] colorspace_2.1-1        polynom_1.4-1           ggnetwork_0.5.13       
##  [58] ROCR_1.0-11             reticulate_1.41.0       spatstat.data_3.1-4    
##  [61] magrittr_2.0.3          lmtest_0.9-40           later_1.4.1            
##  [64] viridis_0.6.5           ggtree_3.8.2            lattice_0.21-8         
##  [67] spatstat.geom_3.3-5     NMF_0.28                future.apply_1.11.3    
##  [70] SparseM_1.84-2          scattermore_1.2         shadowtext_0.1.4       
##  [73] cowplot_1.1.3           RcppAnnoy_0.0.22        pillar_1.10.1          
##  [76] nlme_3.1-162            sna_2.8                 iterators_1.0.14       
##  [79] gridBase_0.4-7          caTools_1.18.3          compiler_4.3.1         
##  [82] RSpectra_0.16-2         stringi_1.8.4           tensor_1.5             
##  [85] plyr_1.8.9              crayon_1.5.3            abind_1.4-8            
##  [88] gridGraphics_0.5-1      locfit_1.5-9.8          sp_2.2-0               
##  [91] graphlayouts_1.2.2      bit_4.5.0.1             fastmatch_1.1-6        
##  [94] codetools_0.2-20        bslib_0.9.0             GetoptLong_1.0.5       
##  [97] plotly_4.10.4           mime_0.12               splines_4.3.1          
## [100] circlize_0.4.16         Rcpp_1.0.14             quantreg_6.00          
## [103] HDO.db_0.99.1           cellranger_1.1.0        gridtext_0.1.5         
## [106] blob_1.2.4              clue_0.3-66             fs_1.6.5               
## [109] listenv_0.9.1           ggsignif_0.6.4          ggplotify_0.1.2        
## [112] Matrix_1.6-5            tzdb_0.4.0              svglite_2.1.3          
## [115] network_1.19.0          tweenr_2.0.3            pkgconfig_2.0.3        
## [118] tools_4.3.1             cachem_1.1.0            RSQLite_2.3.9          
## [121] viridisLite_0.4.2       DBI_1.2.3               fastmap_1.2.0          
## [124] rmarkdown_2.29          scales_1.3.0            ica_1.0-3              
## [127] broom_1.0.7             sass_0.4.9              coda_0.19-4.1          
## [130] FNN_1.1.4.1             dotCall64_1.2           RANN_2.6.2             
## [133] farver_2.1.2            tidygraph_1.3.1         scatterpie_0.2.4       
## [136] yaml_2.3.10             cli_3.6.4               leiden_0.4.3.1         
## [139] lifecycle_1.0.4         uwot_0.2.3              presto_1.0.0           
## [142] backports_1.5.0         BiocParallel_1.34.2     timechange_0.3.0       
## [145] gtable_0.3.6            rjson_0.2.23            ggridges_0.5.6         
## [148] progressr_0.15.1        limma_3.56.2            parallel_4.3.1         
## [151] ape_5.8-1               jsonlite_1.9.0          bitops_1.0-9           
## [154] bit64_4.6.0-1           Rtsne_0.17              yulab.utils_0.2.0      
## [157] spatstat.utils_3.1-2    BiocNeighbors_1.18.0    zip_2.3.2              
## [160] jquerylib_0.1.4         GOSemSim_2.26.1         spatstat.univar_3.1-1  
## [163] lazyeval_0.2.2          shiny_1.10.0            htmltools_0.5.8.1      
## [166] GO.db_3.17.0            sctransform_0.4.1       glue_1.8.0             
## [169] spam_2.11-1             XVector_0.40.0          RCurl_1.98-1.12        
## [172] rprojroot_2.0.4         treeio_1.24.3           gridExtra_2.3          
## [175] R6_2.6.1                labeling_0.4.3          cluster_2.1.4          
## [178] rngtools_1.5.2          aplot_0.2.5             statnet.common_4.11.0  
## [181] vipor_0.4.7             DelayedArray_0.26.7     tidyselect_1.2.1       
## [184] ggforce_0.4.2           xml2_1.3.7              AnnotationDbi_1.62.2   
## [187] future_1.34.0           munsell_0.5.1           KernSmooth_2.23-21     
## [190] htmlwidgets_1.6.4       fgsea_1.26.0            rlang_1.1.5            
## [193] spatstat.sparse_3.1-0   spatstat.explore_3.3-4  beeswarm_0.4.0